Model Overview

A pre-trained model for simultaneous segmentation and classification of nuclei within multi-tissue histology images based on CoNSeP data. The details of the model can be found in [1].

The model is trained to simultaneously segment and classify nuclei, and a two-stage training approach is utilized:

Initialize the model with pre-trained weights, and train the decoder only for 50 epochs.
Finetune all layers for another 50 epochs.

There are two training modes in total. If \"original\" mode is specified, [270, 270] and [80, 80] are used for patch_size and out_size respectively. If \"fast\" mode is specified, [256, 256] and [164, 164] are used for patch_size and out_size respectively. The results shown below are based on the \"fast\" mode.

In this bundle, the first stage is trained with pre-trained weights from some internal data. The original author's repo and torchvison also provide pre-trained weights but for non-commercial use.\nEach user is responsible for checking the content of models/datasets and the applicable licenses and determining if suitable for the intended use.

If you want to train the first stage with pre-trained weights, just specify --network_def#pretrained_url <your pretrain weights URL> in the training command below, such as ImageNet.

$\"Model$

Data

The training data is from https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet/.

Target: segment instance-level nuclei and classify the nuclei type
Task: Segmentation and classification
Modality: RGB images
Size: 41 image tiles (2009 patches)

The provided labelled data was partitioned, based on the original split, into training (27 tiles) and testing (14 tiles) datasets.

You can download the dataset by using this command:

wget https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet/consep_dataset.zip\nunzip consep_dataset.zip\n

Preprocessing

After download the datasets, please run scripts/prepare_patches.py to prepare patches from tiles. Prepared patches are saved in <your concep dataset path>/Prepared. The implementation is referring to https://github.com/vqdang/hover_net. The command is like:

\n\">

python scripts/prepare_patches.py --root <your concep dataset path>\n

Training configuration

This model utilized a two-stage approach. The training was performed with the following:

GPU: At least 24GB of GPU memory.
Actual Model Input: 256 x 256
AMP: True
Optimizer: Adam
Learning Rate: 1e-4
Loss: HoVerNetLoss
Dataset Manager: CacheDataset

Memory Consumption Warning

If you face memory issues with CacheDataset, you can either switch to a regular Dataset class or lower the caching rate cache_rate in the configurations within range [0, 1] to minimize the System RAM requirements.

Input

Input: RGB images

Output

Output: a dictionary with the following keys:

nucleus_prediction: predict whether or not a pixel belongs to the nuclei or background
horizontal_vertical: predict the horizontal and vertical distances of nuclear pixels to their centres of mass
type_prediction: predict the type of nucleus for each pixel

Performance

The achieved metrics on the validation data are:

Fast mode:

Binary Dice: 0.8291
PQ: 0.4973
F1d: 0.7417

Note:

Binary Dice is calculated based on the whole input. PQ and F1d were calculated from https://github.com/vqdang/hover_net#inference.
This bundle is non-deterministic because of the bilinear interpolation used in the network. Therefore, reproducing the training process may not get exactly the same performance.\nPlease refer to https://pytorch.org/docs/stable/notes/randomness.html#reproducibility for more details about reproducibility.

Training Loss and Dice

stage1:\n $\"A$

stage2:\n $\"A$

Validation Dice

stage1:

$\"A$

stage2:

$\"A$

TensorRT speedup

This bundle supports acceleration with TensorRT. The table below displays the speedup ratios observed on an A100 80G GPU. Please note that 32-bit precision models are benchmarked with tf32 weight format.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

method	torch_tf32(ms)	torch_amp(ms)	trt_tf32(ms)	trt_fp16(ms)	speedup amp	speedup tf32	speedup fp16	amp vs fp16
model computation	24.55	20.14	10.85	5.63	1.22	2.26	4.36	3.58
end2end	3451	3312	1318	878	1.04	2.62	3.93	3.77

Where:

model computation means the speedup ratio of model's inference with a random input without preprocessing and postprocessing
end2end means run the bundle end-to-end with the TensorRT based model.
torch_tf32 and torch_amp are for the PyTorch models with or without amp mode.
trt_tf32 and trt_fp16 are for the TensorRT based models converted in corresponding precision.
speedup amp, speedup tf32 and speedup fp16 are the speedup ratios of corresponding models versus the PyTorch float32 model
amp vs fp16 is the speedup ratio between the PyTorch amp model and the TensorRT float16 based model.

This result is benchmarked under:

TensorRT: 10.3.0+cuda12.6
Torch-TensorRT Version: 2.4.0
CPU Architecture: x86-64
OS: ubuntu 20.04
Python version:3.10.12
CUDA version: 12.6
GPU models and configuration: A100 80G

MONAI Bundle Commands

In addition to the Pythonic APIs, a few command line interfaces (CLI) are provided to interact with the bundle. The CLI supports flexible use cases, such as overriding configs at runtime and predefining arguments in a file.

For more details usage instructions, visit the MONAI Bundle Configuration Page.

Execute training, the evaluation during the training were evaluated on patches:

Please note that if the default dataset path is not modified with the actual path in the bundle config files, you can also override it by using --dataset_dir:

Run first stage

\n\">

python -m monai.bundle run --config_file configs/train.json --stage 0 --dataset_dir <actual dataset path>\n

Run second stage

\n\">

python -m monai.bundle run --config_file configs/train.json --network_def#freeze_encoder False --stage 1 --dataset_dir <actual dataset path>\n

Override the `train` config to execute multi-GPU training:

Run first stage

torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run --config_file \"['configs/train.json','configs/multi_gpu_train.json']\" --batch_size 8 --network_def#freeze_encoder True --stage 0\n

Run second stage

torchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run --config_file \"['configs/train.json','configs/multi_gpu_train.json']\" --batch_size 4 --network_def#freeze_encoder False --stage 1\n

Override the `train` config to execute evaluation with the trained model, here we evaluated dice from the whole input instead of the patches:

python -m monai.bundle run --config_file \"['configs/train.json','configs/evaluate.json']\"\n

Execute inference:

python -m monai.bundle run --config_file configs/inference.json\n

Execute inference with the TensorRT model:

python -m monai.bundle run --config_file \"['configs/inference.json', 'configs/inference_trt.json']\"\n

References

[1] Simon Graham, Quoc Dang Vu, Shan E Ahmed Raza, Ayesha Azam, Yee Wah Tsang, Jin Tae Kwak, Nasir Rajpoot, Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images, Medical Image Analysis, 2019 https://doi.org/10.1016/j.media.2019.101563

License

Licensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0\n

Unless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.

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The details of the model can be found in [1].\n\nThe model is trained to simultaneously segment and classify nuclei, and a two-stage training approach is utilized:\n\n- Initialize the model with pre-trained weights, and train the decoder only for 50 epochs.\n- Finetune all layers for another 50 epochs.\n\nThere are two training modes in total. If \"original\" mode is specified, [270, 270] and [80, 80] are used for `patch_size` and `out_size` respectively. If \"fast\" mode is specified, [256, 256] and [164, 164] are used for `patch_size` and `out_size` respectively. The results shown below are based on the \"fast\" mode.\n\nIn this bundle, the first stage is trained with pre-trained weights from some internal data. The [original author's repo](https://github.com/vqdang/hover_net) and [torchvison](https://pytorch.org/vision/stable/_modules/torchvision/models/resnet.html#ResNet18_Weights) also provide pre-trained weights but for non-commercial use.\nEach user is responsible for checking the content of models/datasets and the applicable licenses and determining if suitable for the intended use.\n\nIf you want to train the first stage with pre-trained weights, just specify `--network_def#pretrained_url ` in the training command below, such as [ImageNet](https://download.pytorch.org/models/resnet18-f37072fd.pth).\n\n![Model workflow](https://developer.download.nvidia.com/assets/Clara/Images/monai_hovernet_pipeline.png)\n\n### Data\n\nThe training data is from .\n\n- Target: segment instance-level nuclei and classify the nuclei type\n- Task: Segmentation and classification\n- Modality: RGB images\n- Size: 41 image tiles (2009 patches)\n\nThe provided labelled data was partitioned, based on the original split, into training (27 tiles) and testing (14 tiles) datasets.\n\nYou can download the dataset by using this command:\n\n```\nwget https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet/consep_dataset.zip\nunzip consep_dataset.zip\n```\n\n#### Preprocessing\n\nAfter download the [datasets](https://warwick.ac.uk/fac/cross_fac/tia/data/hovernet/consep_dataset.zip), please run `scripts/prepare_patches.py` to prepare patches from tiles. Prepared patches are saved in ``/Prepared. The implementation is referring to . The command is like:\n\n```\npython scripts/prepare_patches.py --root \n```\n\n### Training configuration\n\nThis model utilized a two-stage approach. The training was performed with the following:\n\n- GPU: At least 24GB of GPU memory.\n- Actual Model Input: 256 x 256\n- AMP: True\n- Optimizer: Adam\n- Learning Rate: 1e-4\n- Loss: HoVerNetLoss\n- Dataset Manager: CacheDataset\n\n#### Memory Consumption Warning\n\nIf you face memory issues with CacheDataset, you can either switch to a regular Dataset class or lower the caching rate `cache_rate` in the configurations within range [0, 1] to minimize the System RAM requirements.\n\n### Input\n\nInput: RGB images\n\n### Output\n\nOutput: a dictionary with the following keys:\n\n1. nucleus_prediction: predict whether or not a pixel belongs to the nuclei or background\n2. horizontal_vertical: predict the horizontal and vertical distances of nuclear pixels to their centres of mass\n3. type_prediction: predict the type of nucleus for each pixel\n\n### Performance\n\nThe achieved metrics on the validation data are:\n\nFast mode:\n- Binary Dice: 0.8291\n- PQ: 0.4973\n- F1d: 0.7417\n\nNote:\n- Binary Dice is calculated based on the whole input. PQ and F1d were calculated from https://github.com/vqdang/hover_net#inference.\n- This bundle is non-deterministic because of the bilinear interpolation used in the network. Therefore, reproducing the training process may not get exactly the same performance.\n Please refer to https://pytorch.org/docs/stable/notes/randomness.html#reproducibility for more details about reproducibility.\n\n##### Training Loss and Dice\n\nstage1:\n![A graph showing the training loss and the mean dice over 50 epochs in stage1](https://developer.download.nvidia.com/assets/Clara/Images/monai_pathology_segmentation_classification_train_stage0_v2.png)\n\nstage2:\n![A graph showing the training loss and the mean dice over 50 epochs in stage2](https://developer.download.nvidia.com/assets/Clara/Images/monai_pathology_segmentation_classification_train_stage1_v2.png)\n\n##### Validation Dice\n\nstage1:\n\n![A graph showing the validation mean dice over 50 epochs in stage1](https://developer.download.nvidia.com/assets/Clara/Images/monai_pathology_segmentation_classification_val_stage0_v2.png)\n\nstage2:\n\n![A graph showing the validation mean dice over 50 epochs in stage2](https://developer.download.nvidia.com/assets/Clara/Images/monai_pathology_segmentation_classification_val_stage1_v2.png)\n\n##### TensorRT speedup\n\nThis bundle supports acceleration with TensorRT. The table below displays the speedup ratios observed on an A100 80G GPU. Please note that 32-bit precision models are benchmarked with tf32 weight format.\n\n| method | torch_tf32(ms) | torch_amp(ms) | trt_tf32(ms) | trt_fp16(ms) | speedup amp | speedup tf32 | speedup fp16 | amp vs fp16|\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n| model computation | 24.55 | 20.14 | 10.85 | 5.63 | 1.22 | 2.26 | 4.36 | 3.58 |\n| end2end | 3451 | 3312 | 1318 | 878 | 1.04 | 2.62 | 3.93 | 3.77 |\n\nWhere:\n- `model computation` means the speedup ratio of model's inference with a random input without preprocessing and postprocessing\n- `end2end` means run the bundle end-to-end with the TensorRT based model.\n- `torch_tf32` and `torch_amp` are for the PyTorch models with or without `amp` mode.\n- `trt_tf32` and `trt_fp16` are for the TensorRT based models converted in corresponding precision.\n- `speedup amp`, `speedup tf32` and `speedup fp16` are the speedup ratios of corresponding models versus the PyTorch float32 model\n- `amp vs fp16` is the speedup ratio between the PyTorch amp model and the TensorRT float16 based model.\n\nThis result is benchmarked under:\n- TensorRT: 10.3.0+cuda12.6\n- Torch-TensorRT Version: 2.4.0\n- CPU Architecture: x86-64\n- OS: ubuntu 20.04\n- Python version:3.10.12\n- CUDA version: 12.6\n- GPU models and configuration: A100 80G\n\n### MONAI Bundle Commands\n\nIn addition to the Pythonic APIs, a few command line interfaces (CLI) are provided to interact with the bundle. The CLI supports flexible use cases, such as overriding configs at runtime and predefining arguments in a file.\n\nFor more details usage instructions, visit the [MONAI Bundle Configuration Page](https://docs.monai.io/en/latest/config_syntax.html).\n\n##### Execute training, the evaluation during the training were evaluated on patches:\n\nPlease note that if the default dataset path is not modified with the actual path in the bundle config files, you can also override it by using `--dataset_dir`:\n\n- Run first stage\n\n```\npython -m monai.bundle run --config_file configs/train.json --stage 0 --dataset_dir \n```\n\n- Run second stage\n\n```\npython -m monai.bundle run --config_file configs/train.json --network_def#freeze_encoder False --stage 1 --dataset_dir \n```\n\n##### Override the `train` config to execute multi-GPU training:\n\n- Run first stage\n\n```\ntorchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run --config_file \"['configs/train.json','configs/multi_gpu_train.json']\" --batch_size 8 --network_def#freeze_encoder True --stage 0\n```\n\n- Run second stage\n\n```\ntorchrun --standalone --nnodes=1 --nproc_per_node=2 -m monai.bundle run --config_file \"['configs/train.json','configs/multi_gpu_train.json']\" --batch_size 4 --network_def#freeze_encoder False --stage 1\n```\n\n##### Override the `train` config to execute evaluation with the trained model, here we evaluated dice from the whole input instead of the patches:\n\n```\npython -m monai.bundle run --config_file \"['configs/train.json','configs/evaluate.json']\"\n```\n\n##### Execute inference:\n\n```\npython -m monai.bundle run --config_file configs/inference.json\n```\n\n##### Execute inference with the TensorRT model:\n\n```\npython -m monai.bundle run --config_file \"['configs/inference.json', 'configs/inference_trt.json']\"\n```\n\n## References\n\n[1] Simon Graham, Quoc Dang Vu, Shan E Ahmed Raza, Ayesha Azam, Yee Wah Tsang, Jin Tae Kwak, Nasir Rajpoot, Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images, Medical Image Analysis, 2019 https://doi.org/10.1016/j.media.2019.101563\n\n## License\n\nCopyright (c) MONAI Consortium\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the 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They are capable of tracking your browser across other sites and building up a profile of your interests. This may impact the content and messages you see on other websites you visit. If you do not allow these cookies you may not be able to use or see these sharing tools.","alwaysActive","order",11,"cookies",[],"hosts",[],"C0004","Advertising Cookies","These cookies record your visit to our websites, the pages you have visited and the links you have followed to influence the advertisements that you see on other websites. These cookies and the information they collect may be managed by other companies, including our advertising partners, and may be used to build a profile of your interests and show you relevant advertising on other sites. We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612],[476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492],{"_493":586,"_304":586,"_258":259,"_466":587},{"_493":582,"_304":582,"_258":259,"_466":583},{"_493":577,"_304":577,"_258":259,"_466":578},{"_493":573,"_304":573,"_258":259,"_466":574},{"_493":568,"_304":568,"_258":259,"_466":569},{"_493":563,"_304":563,"_258":259,"_466":564},{"_493":555,"_304":555,"_258":259,"_466":556},{"_493":547,"_304":547,"_258":259,"_466":548},{"_493":542,"_304":542,"_258":259,"_466":543},{"_493":537,"_304":537,"_258":259,"_466":538},{"_493":532,"_304":532,"_258":259,"_466":533},{"_493":527,"_304":527,"_258":259,"_466":528},{"_493":522,"_304":522,"_258":259,"_466":523},{"_493":515,"_304":515,"_258":259,"_466":516},{"_493":510,"_304":510,"_258":259,"_466":511},{"_493":503,"_304":503,"_258":259,"_466":504},{"_493":494,"_304":494,"_258":259,"_466":495},"hostName","amazon-adsystem.com",[496,497],{"_83":502,"_499":494,"_500":31,"_258":501},{"_83":498,"_499":494,"_500":31,"_258":501},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[505,506],{"_83":509,"_499":503,"_500":31,"_258":508},{"_83":507,"_499":503,"_500":31,"_258":508},"IDE","This domain is owned by Doubleclick (Google). 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This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_bc_session_id","ttcsid_XXXXXXXXXXXXXXXXXXXXCategory","This cookie name is associated with TikTok which is used to track your activity on the platform, including how you interact with their embedded services on other websites.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","_fbp","Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers","_zitok","_twpid","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. 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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707],[654,655,656,657,658],{"_493":568,"_304":568,"_258":259,"_466":678},{"_493":542,"_304":542,"_258":259,"_466":673},{"_493":668,"_304":668,"_258":259,"_466":669},{"_493":663,"_304":663,"_258":259,"_466":664},{"_493":659,"_304":659,"_258":259,"_466":660},"nvidia.partners",[661],{"_83":662,"_499":659,"_500":31,"_258":536},"edge","app.hushly.com",[665],{"_83":666,"_499":663,"_500":31,"_258":667},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[670],{"_83":671,"_499":668,"_500":31,"_258":672},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794],[746,747,748,749],{"_493":761,"_304":761,"_258":259,"_466":762},{"_493":756,"_304":756,"_258":259,"_466":757},{"_493":555,"_304":555,"_258":259,"_466":753},{"_493":668,"_304":668,"_258":259,"_466":750},[751],{"_83":752,"_499":668,"_500":179,"_258":672},"_gh_sess",[754],{"_83":755,"_499":555,"_500":179,"_258":562},"YSC","bizible.com",[758],{"_83":759,"_499":756,"_500":31,"_258":760},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[763],{"_83":764,"_499":761,"_500":31,"_258":765},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_83":836,"_499":614,"_500":31,"_258":837},{"_83":821,"_499":835,"_500":179,"_258":822},{"_83":804,"_499":617,"_500":31,"_258":729},{"_83":804,"_499":717,"_500":31,"_258":729},{"_83":833,"_499":614,"_500":31,"_258":834},{"_83":821,"_499":620,"_500":179,"_258":822},{"_83":831,"_499":614,"_500":31,"_258":832},{"_83":821,"_499":617,"_500":179,"_258":822},{"_83":829,"_499":716,"_500":31,"_258":830},{"_83":821,"_499":738,"_500":179,"_258":822},{"_83":801,"_499":617,"_500":31,"_258":803},{"_83":827,"_499":614,"_500":179,"_258":828},{"_83":825,"_499":614,"_500":31,"_258":826},{"_83":823,"_499":614,"_500":179,"_258":824},{"_83":821,"_499":712,"_500":179,"_258":822},{"_83":819,"_499":614,"_500":31,"_258":820},{"_83":817,"_499":614,"_500":31,"_258":818},{"_83":815,"_499":614,"_500":179,"_258":816},{"_83":813,"_499":721,"_500":31,"_258":814},{"_83":801,"_499":712,"_500":31,"_258":803},{"_83":811,"_499":614,"_500":31,"_258":812},{"_83":809,"_499":614,"_500":179,"_258":810},{"_83":807,"_499":614,"_500":31,"_258":808},{"_83":805,"_499":614,"_500":31,"_258":806},{"_83":804,"_499":614,"_500":31,"_258":729},{"_83":801,"_499":802,"_500":31,"_258":803},{"_83":799,"_499":614,"_500":31,"_258":800},{"_83":797,"_499":614,"_500":179,"_258":798},{"_83":795,"_499":614,"_500":31,"_258":796},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. It contains the current session ID, whether the session is excluded due to sampling, and the expiration date of the session. The cookie is extended for an extra 15 minutes every time the user interacts with the website, up to the maximum user session duration (4 hours).","nvweb_A","_ga","This cookie name is associated with Google Universal Analytics - which is a significant update to Google's more commonly used analytics service. This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","tp","This cookie is associated with Adobe Analytics site tracking. It registers if the website's scroll-depth detection is active. This function remembers how far the user has scrolled on the current session across the website's sub-pages.","_hly_vid","This cookie _hly_vid is used to record website and server performance with each visit to the site. The data is aggregated and analyzed; therefore, it remains anonymous upon use.","_session_id","This cookie creates an interim session ID used as an in-session user ID.\n","s_cc","This cookie is associated with the Adobe Site Catalyst. It determines whether cookies are enabled in the web browser.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","_hjSessionUser_xxxxxx","Hotjar cookie that is set when a user first lands on a page with the Hotjar script. 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Model Overview

Data

Preprocessing

Training configuration

Memory Consumption Warning

Input

Output

Performance

Training Loss and Dice

Validation Dice

TensorRT speedup

MONAI Bundle Commands

Execute training, the evaluation during the training were evaluated on patches:

Override the train config to execute multi-GPU training:

Override the train config to execute evaluation with the trained model, here we evaluated dice from the whole input instead of the patches:

Execute inference:

Execute inference with the TensorRT model:

References

License

Override the `train` config to execute multi-GPU training:

Override the `train` config to execute evaluation with the trained model, here we evaluated dice from the whole input instead of the patches:

Name	Size	Updated	Actions




LICENSE	11.09 KB	February 28, 2025 UTC	curl wget Download